Package mdp :: Package nodes :: Class FANode

Class FANode

Perform Factor Analysis.

The current implementation should be most efficient for long
data sets: the sufficient statistics are collected in the
training phase, and all EM-cycles are performed at
its end.

The ``execute`` method returns the Maximum A Posteriori estimate
of the latent variables. The ``generate_input`` method generates
observations from the prior distribution.

**Internal variables of interest**

  ``self.mu``
      Mean of the input data (available after training)

  ``self.A``
      Generating weights (available after training)

  ``self.E_y_mtx``
      Weights for Maximum A Posteriori inference

  ``self.sigma``
      Vector of estimated variance of the noise
      for all input components

More information about Factor Analysis can be found in
Max Welling's classnotes:
http://www.ics.uci.edu/~welling/classnotes/classnotes.html ,
in the chapter 'Linear Models'.

Instance Methods

[hide private]

__init__(self, tol=0.0001, max_cycles=100, verbose=False, input_dim=None, output_dim=None, dtype=None)
:Parameters:...

_execute(self, x)

_stop_training(self)

_train(self, x)

execute(self, x)
Process the data contained in `x`.

generate_input(self, len_or_y=1, noise=False)
Generate data from the prior distribution.

stop_training(self)
Stop the training phase.

train(self, x)
Update the internal structures according to the input data `x`.

Inherited from unreachable.newobject: __long__, __native__, __nonzero__, __unicode__, next

Inherited from object: __delattr__, __format__, __getattribute__, __hash__, __new__, __reduce__, __reduce_ex__, __setattr__, __sizeof__, __subclasshook__

Inherited from Node

__add__(self, other)

__call__(self, x, *args, **kwargs)
Calling an instance of `Node` is equivalent to calling its `execute` method.

__repr__(self)
repr(x)

__str__(self)
str(x)

_check_input(self, x)

_check_output(self, y)

_check_train_args(self, x, *args, **kwargs)

_get_supported_dtypes(self)
Return the list of dtypes supported by this node.

_get_train_seq(self)

_if_training_stop_training(self)

_inverse(self, x)

_pre_execution_checks(self, x)
This method contains all pre-execution checks.

_pre_inversion_checks(self, y)
This method contains all pre-inversion checks.

_refcast(self, x)
Helper function to cast arrays to the internal dtype.

_set_dtype(self, t)

_set_input_dim(self, n)

_set_output_dim(self, n)

copy(self, protocol=None)
Return a deep copy of the node.

get_current_train_phase(self)
Return the index of the current training phase.

get_dtype(self)
Return dtype.

get_input_dim(self)
Return input dimensions.

get_output_dim(self)
Return output dimensions.

get_remaining_train_phase(self)
Return the number of training phases still to accomplish.

get_supported_dtypes(self)
Return dtypes supported by the node as a list of :numpy:`dtype` objects.

has_multiple_training_phases(self)
Return True if the node has multiple training phases.

inverse(self, y, *args, **kwargs)
Invert `y`.

is_training(self)
Return True if the node is in the training phase, False otherwise.

save(self, filename, protocol=-1)
Save a pickled serialization of the node to `filename`.

set_dtype(self, t)
Set internal structures' dtype.

set_input_dim(self, n)
Set input dimensions.

set_output_dim(self, n)
Set output dimensions.

Static Methods

[hide private]

is_invertible()
Return True if the node can be inverted, False otherwise.

Inherited from Node

is_trainable()
Return True if the node can be trained, False otherwise.

Properties

[hide private]

Inherited from object: __class__

Inherited from Node

_train_seq
List of tuples::

dtype
dtype

input_dim
Input dimensions

output_dim
Output dimensions

supported_dtypes
Supported dtypes

Method Details

[hide private]

init(self, tol=0.0001, max_cycles=100, verbose=False, input_dim=None, output_dim=None, dtype=None)
(Constructor)


:Parameters:
  tol
    tolerance (minimum change in log-likelihood before exiting
    the EM algorithm)
  max_cycles
    maximum number of EM cycles
  verbose
    if true, print log-likelihood during the EM-cycles

Overrides: object.__init__

execute(self, x)

Process the data contained in `x`.

If the object is still in the training phase, the function
`stop_training` will be called.
`x` is a matrix having different variables on different columns
and observations on the rows.

By default, subclasses should overwrite `_execute` to implement
their execution phase. The docstring of the `_execute` method
overwrites this docstring.

Overrides: Node.execute

generate_input(self, len_or_y=1, noise=False)


Generate data from the prior distribution.

If the training phase has not been completed yet, call stop_training.

:Arguments:
  len_or_y
            If integer, it specified the number of observation
            to generate. If array, it is used as a set of samples
            of the latent variables
  noise
            if true, generation includes the estimated noise

is_invertible()
Static Method

Return True if the node can be inverted, False otherwise.

Overrides: Node.is_invertible: (inherited documentation)

stop_training(self)

Stop the training phase.

By default, subclasses should overwrite `_stop_training` to implement
this functionality. The docstring of the `_stop_training` method
overwrites this docstring.

Overrides: Node.stop_training

train(self, x)

Update the internal structures according to the input data `x`.

`x` is a matrix having different variables on different columns
and observations on the rows.

By default, subclasses should overwrite `_train` to implement their
training phase. The docstring of the `_train` method overwrites this
docstring.

Note: a subclass supporting multiple training phases should implement
the *same* signature for all the training phases and document the
meaning of the arguments in the `_train` method doc-string. Having
consistent signatures is a requirement to use the node in a flow.

Overrides: Node.train

Class FANode

__init__(self, tol=0.0001, max_cycles=100, verbose=False, input_dim=None, output_dim=None, dtype=None) (Constructor)

_execute(self, x)

_stop_training(self)

_train(self, x)

execute(self, x)

generate_input(self, len_or_y=1, noise=False)

is_invertible() Static Method

stop_training(self)

train(self, x)

init(self, tol=0.0001, max_cycles=100, verbose=False, input_dim=None, output_dim=None, dtype=None)
(Constructor)

is_invertible()
Static Method